Offerings

01

Knowledge Graphs & AI Architecture

Raw data does not answer questions. Knowledge does — and knowledge requires an architecture: a structured representation of what entities exist, how they relate, what causes what, and where uncertainty lives.

This practice area covers the design and implementation of knowledge graph systems using graph database technology (Neo4j and related platforms), ontology and taxonomy development, semantic data modeling, and the retrieval architectures that make knowledge accessible to both human analysts and AI systems.

Particular focus on engineering and quality domains: failure mode networks, causal graphs, process knowledge, regulatory document architectures, and the actor networks that model people — roles, SMEs, decision authorities, and stakeholders — as first-class nodes alongside the systems they inhabit. Built AI-ready for retrieval-augmented generation (RAG) and agentic workflows.

02

Design & Reliability

Structured product development and reliability engineering for organizations building physical systems that must perform under demanding conditions. The foundation is DRM — Design for Reliability and Manufacturability — a systematic methodology that replaces intuition with structure from voice of customer through production control.

Deep domain experience in electrochemical systems, energy storage, medical device design, and industrial process development. Applicable wherever the underlying problem is a physical system that must be designed, validated, and manufactured to a standard that can be defended.

03

Data & Modeling

For organizations that need to extract meaning from data, build predictive or simulation models, or design the experiments that generate useful data. These are not sequential steps — they are interconnected domains, each capable of informing the others, with rigorous statistical thinking as the foundation throughout.

The entry point depends on what you have: existing data that can constrain a model, or a first-principles simulation that defines what data to collect. What matters is that the logic connecting measurement, model, and experiment is explicit — not assumed.

Some experiments are worth running early and cheaply — a quick feasibility test can confirm a concept or generate the evidence needed to fund the next step. But for higher-stakes work, experimentation is rarely the starting point. Most organizations already know 80% or more of the relevant factors and responses. Surfacing that knowledge first — through data analysis, modeling, or graph mapping of what is known and unknown — is what makes the remaining experiments precise rather than expensive.

04

Coaching & Training

Technical capability that lives only in one person is fragile. The goal of any engagement is that the organization ends with more capacity than it started — not just a solved problem, but the tools to solve the next one.

There is also the reverse. Engineers entering organizations often arrive with more modern computational tools than the organization uses — schools teach MATLAB, Python, and simulation environments while many engineering departments still run critical calculations in spreadsheets. That gap is navigable in both directions: helping individuals use better tools within existing architectures, or helping technical leaders understand what a thoughtful transition looks like and how to build the internal confidence to make it.